scholarly journals Evaluation of North American Asian Citrus Psyllid (Hemiptera: Liviidae) Population Genetics using cytochrome P450 melt curve analysis

2018 ◽  
Author(s):  
Juan F. Macias-Velasco ◽  
Ryan T. Brunson ◽  
Wayne B. Hunter ◽  
Blake R. Bextine
Keyword(s):  
Principal Component Analysis ◽  
Cytochrome P450 ◽  
Genetic Relationships ◽  
Melting Curve ◽  
Principal Component ◽  
Component Analysis ◽  
The United States ◽  
Curve Analysis ◽  
Asian Citrus Psyllid ◽  
Melting Curve Analysis

AbstractThe Asian Citrus Psyllid (ACP) (Diaphorina citri) is a Hemipteran which feeds on Citrus and is the principal vector of the pathogen Candidatus Liberibacter asiaticus (Las); the primary causal agent of Huanglongbing (HLB) or citrus greening disease. Currently HLB is the single greatest threat to the American citrus industry. Previous work has shown that that Cytochrome P450 (CYP4) genes are associated with insecticidal resistance to neonicotinoid insecticides. Infection by Las has also been shown to affect the expression of CYP4 genes. In this study the genetic relationships of ACP populations was evaluated using melting curve analysis of CYP4 genes. Principal component analysis shows the presence of two potential haplotypes. The groups identified by principal component analysis are significantly different from each other (P < 0.05) for all CYP4 genes tested. All populations are most likely within 2-3 mutations of each other. The existence of these mutations sheds light on the potential evolution of ACP in North America. Indicating either coevolution with Las or difference in imidacloprid basal selective pressure in the United States and Mexico.

scholarly journals Research on Economic Periodicity Based on Principal Component-Weighted Distance and Clustering Analysis

2020 ◽  
Vol 214 ◽  
pp. 03003
Author(s):  
Jiayi Yan ◽  
Qian Pu ◽  
Junfei Liu
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Clustering Analysis ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
The United States ◽  
Economic Situation ◽  
Analysis Model ◽  
Weighted Distance

Based on the knowledge of economics, this paper selects 22 macroeconomic indicators that best reflect the overall economic situation of the United States. After differential, logarithmic and exponential preprocessing of the original data, this paper, based on the power spectral analysis model, adaptively identifies the periodicity of the selected economic indicators, and visualize the results. As a result, it screens out 11 indicators with obvious periodicity. In the process of solving the weighted distance based on principal component analysis, correlation test is first conducted on the selected 11 single indicators of periodicity to obtain Pearson correlation heatmap. Then, the principal components are extracted by selecting the first five principal components as the virtual indicators to represent the monthly economic situation, and calculating the weighted distance value between months for visualization. Finally, we select the results of 36 months’ smoothing for analysis, figure out the time intervals with similar economic situation, and verify the conjecture of economic periodicity. Finally, based on K-MEAN clustering analysis, the economic conditions of 352 months are classified into 3 clusters by using the weighted distance after 36 months’ smoothing. From the visualized results, it is found that there are two complete cycles, i.e. red-yellow-blue and red-yellow-blue, which is consistent with the conclusion of principal component analysis model, and proves the existence of economic cycle again. In conclusion, based on the above PCA weighted distance and clustering analysis, it can be concluded that the economic period is around 176 months, in favor of medium long periodicity theory.

scholarly journals A regression and comparative study of United States and South African yield curves using principal component analysis

2018 ◽  
Vol 21 (1) ◽  
Author(s):  
Kavir Patel ◽  
Ashfaaq Mohamed ◽  
Gary W. Van Vuuren
Keyword(s):  
United States ◽  
Principal Component Analysis ◽  
Interest Rates ◽  
South African ◽  
Yield Curve ◽  
Principal Component ◽  
Component Analysis ◽  
The United States ◽  
Yield Curves ◽  
Analysis Technique

Volatile markets and economic environments can significantly distort the shape and smoothness of yield curve movements. This study explores the influence of movements in United States interest rates on South African interest rates. This study aims to identify the main underlying movements present in the United States and South African yield curves and to further determine the dominant factors that are responsible for driving South African interest rate movements. The principal settings for the study were the United States and South African markets representing, respectively, a developed and developing market. Principal component analysis was used to discern the major drivers of developing and developed market interest rates. The findings show that the principal component analysis technique is able to effectively classify and quantify the movements of yield curves across both markets in terms of three main factors, namely level, slope and curvature shifts. During certain periods, South African yield curve changes were largely driven by variations in United States interest rates and the rand/dollar exchange rate. Results also demonstrated that a volatile market and economic environment can significantly distort the shape and smoothness of yield curve movements.

scholarly journals Identifying the Key Barriers to Promote Sustainable Construction in the United States: A Principal Component Analysis

2020 ◽  
Vol 12 (12) ◽  
pp. 5088
Author(s):  
Ali Karji ◽  
Mostafa Namian ◽  
Mohammadsoroush Tafazzoli
Keyword(s):  
United States ◽  
Principal Component Analysis ◽  
Construction Industry ◽  
Current Practice ◽  
Principal Component ◽  
Component Analysis ◽  
The United States ◽  
Sustainable Construction ◽  
Adverse Impacts ◽  
Industry Professionals

The need to build more facilities has intensified the inherited adverse impacts of the construction industry on the triple bottom lines of sustainability (i.e., people, planet, and profit). The current practice of sustainability in the construction industry is far from reaching the targeted green goals. In order to foster these endeavors, this study aims to explore sustainable construction barriers in the United States. To achieve the objective, first, 12 sustainability barriers were identified based on an excessive and comprehensive literature review and solicitation of experts’ opinions to validate the barriers. Next, a questionnaire survey was developed and distributed among 135 industry professionals to evaluate the relative importance of factors. To offer a practical solution, principal component analysis (PCA) was used to analyze the data and find the most effective barriers. The results show that four major barriers, including (1) pre-construction constraints, (2) managerial constraints, (3) legislative constraints, and (4) financial and planning constraints are the most influential challenges that the industry faces to foster sustainable construction. Practical solutions are suggested to tackle sustainable construction barriers. The findings of this study are beneficial to the architecture, engineering, and construction (AEC) industry members along with owners and policymakers.

scholarly journals Evaluating STEM-Based Sustainability Understanding: A Cognitive Mapping Approach

2021 ◽  
Vol 13 (14) ◽  
pp. 8074
Author(s):  
Elizabeth L. Petrun Sayers ◽  
Christopher A. Craig ◽  
Emily Skonicki ◽  
Grace Gahlon ◽  
Susan Gilbertz ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Student Outcomes ◽  
Cognitive Mapping ◽  
Assessment Tool ◽  
Principal Component ◽  
Component Analysis ◽  
The United States ◽  
Learning Assessment ◽  
And Mathematics ◽  
Sustainability Curriculum

Management education holds promise for addressing deficiencies in interuniversity science, technology, engineering, and mathematics (STEM), as well as sustainability curricula. Accordingly, we designed, developed, implemented, and longitudinally evaluated interdisciplinary STEM-based curricula in the United States. Students in five sections of business management courses and two sections of STEM courses received a STEM-based sustainability intervention (i.e., an interdisciplinary STEM and sustainability module). To assess student outcomes following the intervention and examine the feasibility of cognitive mapping as a student learning assessment tool, we implemented a pre- and post-course modified cognitive mapping assessment in treatment and comparison courses. To interpret the results, we ran descriptives, correlations, paired sample t tests, and principal component analysis. The t tests suggest that when all coding categories are considered, those participating in curricular interventions listed significantly more sustainability terms. The principal component analysis results demonstrate that treatment courses improved variability explained by 7.23% between pre- and post-tests but declined by 8.22% for comparison courses. Overall, linkages became stronger between parent code categories for treatment courses and weaker for comparison courses. These findings add to existing research related to cognitive mapping and demonstrate the ability of the method to capture changes in student outcomes after exposure to STEM-based sustainability curriculum.

Early Detection of C-130 Aircraft Engine Malfunction by Principal Component Analysis of the Wear Metals in C-130 Engine Oil

1989 ◽  
Vol 43 (2) ◽  
pp. 202-208 ◽  
Author(s):  
Darrell O. Hancock ◽  
Robert E. Synovec
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Aircraft Engine ◽  
Atomic Emission ◽  
Analysis Data ◽  
Component Analysis ◽  
The United States ◽  
Engine Oil ◽  
Oil Analysis ◽  
Engine Components

Spectrophotometry analysis of engine oil for wear metal to detect engine malfunctions and failing engine components is the basis of the United States Air Force Spectrophotometric Oil Analysis Program (SOAP). This program was abandoned for C-130 transport aircraft because of difficulties in correlating the atomic emission spectroscopy data with engine problems. Principal component analysis (PCA), a factor analysis method, reveals information and structure not previously apparent in the C-130 oil analysis data. These results suggest that the C-130 SOAP program could be made viable with the significant advantages obtained through incorporation of PCA.

Psychometric properties of the WHOQOL-BREF amongst wounded, injured, and ill members of the United States military

2017 ◽  
Vol 22 (2) ◽  
pp. 124-135 ◽  
Author(s):  
Keston Lindsay ◽  
Michelle Ferrer ◽  
Ronald Davis ◽  
David Nichols
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Military Personnel ◽  
Psychological Health ◽  
Principal Component ◽  
Psychological Trauma ◽  
Component Analysis ◽  
The United States ◽  
Content Type ◽  
Global War

Purpose Advances in military medical care have facilitated a reduction of fatalities in the global war on terror, relative to previous conflicts. The physical and psychological trauma of returning personnel remain a challenge, and poor physical and psychological health have been shown to affect quality of life (QOL). The purpose of this paper is to validate the WHOQOL-BREF questionnaire amongst wounded, injured and ill military personnel, and to determine the characteristics of distinct groups found in this sample. Design/methodology/approach In total, 52 male and female military personnel (34.69+7.63 years, n=51) completed 24 items of the WHOQOL-BREF. Principal component analysis using the direct oblimin rotation was used to determine the factor structure of the WHOQOL-BREF and k-means cluster analysis was used to determine QOL characteristics of the separate groups. Findings The WHOQOL-BREF is a reliable tool for measuring QOL for American military personnel. However, the psychometric structure of the WHOQOL-BREF in this sample differed from the original domains. The first cluster analysis based on the original domains produced two clusters: a group of 12 that had poor QOL, and a group of 40 that had relatively good QOL except for the physical domain. The second cluster analysis differed in independence and access/social support only. Research limitations/implications Although the sample was small for principal component analysis, the investigators chose to proceed with this procedure, because objective indicators such as measures of sampling adequacy and communalities met or exceeded acceptable thresholds. Originality/value Rehabilitation programs for military ill, injured and wounded should contain components that promote independence and self-actualization.

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